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                            <div style="color:gray; word-break: break-all; font-size:12px;">原英文版地址: <a href="https://www.elastic.co/guide/en/elasticsearch/reference/7.7/frozen-indices.html" rel="nofollow" target="_blank">https://www.elastic.co/guide/en/elasticsearch/reference/7.7/frozen-indices.html</a>, 原文档版权归 www.elastic.co 所有<br/>本地英文版地址: <a href="../en/frozen-indices.html" rel="nofollow" target="_blank">../en/frozen-indices.html</a></div>
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<strong>重要</strong>: 此版本不会发布额外的bug修复或文档更新。最新信息请参考 <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html" rel="nofollow">当前版本文档</a>。
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<a id="frozen-indices"></a>Frozen indices<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/frozen-indices.asciidoc">edit</a><a class="xpack_tag" href="https://www.elastic.co/subscriptions"></a>
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<p>Elasticsearch indices keep some data structures in memory to allow you to search them
efficiently and to index into them. If you have a lot of indices then the
memory required for these data structures can add up to a significant amount.
For indices that are searched frequently it is better to keep these structures
in memory because it takes time to rebuild them. However, you might access some
of your indices so rarely that you would prefer to release the corresponding
memory and rebuild these data structures on each search.</p>
<p>For example, if you are using time-based indices to store log messages or time
series data then it is likely that older indices are searched much less often
than the more recent ones. Older indices also receive no indexing requests.
Furthermore, it is usually the case that searches of older indices are for
performing longer-term analyses for which a slower response is acceptable.</p>
<p>If you have such indices then they are good candidates for becoming <em>frozen
indices</em>. Elasticsearch builds the transient data structures of each shard of a frozen
index each time that shard is searched, and discards these data structures as
soon as the search is complete. Because Elasticsearch does not maintain these transient
data structures in memory, frozen indices consume much less heap than normal
indices. This allows for a much higher disk-to-heap ratio than would otherwise
be possible.</p>
<p>You can freeze the index using the <a class="xref" href="freeze-index-api.html" title="Freeze index API">Freeze Index API</a>.</p>
<p>Searches performed on frozen indices use the small, dedicated,
<a class="xref" href="modules-threadpool.html#search-throttled"><code class="literal">search_throttled</code> threadpool</a> to control the number of
concurrent searches that hit frozen shards on each node. This limits the amount
of extra memory required for the transient data structures corresponding to
frozen shards, which consequently protects nodes against excessive memory
consumption.</p>
<p>Frozen indices are read-only: you cannot index into them.</p>
<p>Searches on frozen indices are expected to execute slowly. Frozen indices are
not intended for high search load. It is possible that a search of a frozen
index may take seconds or minutes to complete, even if the same searches
completed in milliseconds when the indices were not frozen.</p>
<p>To make a frozen index writable again, use the <a class="xref" href="unfreeze-index-api.html" title="Unfreeze index API">Unfreeze Index API</a>.</p>
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